Project Name:Advanced House Price Prediction

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
pd.pandas.set_option('display.max_columns',None)

ds=pd.read_csv('train.csv')

print(ds.shape)

(1460, 81)

ds.head()

	Id	MSSubClass	MSZoning	LotFrontage	LotArea	Street	Alley	LotShape	LandContour	Utilities	LotConfig	LandSlope	Neighborhood	Condition1	Condition2	BldgType	HouseStyle	OverallQual	OverallCond	YearBuilt	YearRemodAdd	RoofStyle	RoofMatl	Exterior1st	Exterior2nd	MasVnrType	MasVnrArea	ExterQual	ExterCond	Foundation	BsmtQual	BsmtCond	BsmtExposure	BsmtFinType1	BsmtFinSF1	BsmtFinType2	BsmtFinSF2	BsmtUnfSF	TotalBsmtSF	Heating	HeatingQC	CentralAir	Electrical	1stFlrSF	2ndFlrSF	LowQualFinSF	GrLivArea	BsmtFullBath	BsmtHalfBath	FullBath	HalfBath	BedroomAbvGr	KitchenAbvGr	KitchenQual	TotRmsAbvGrd	Functional	Fireplaces	FireplaceQu	GarageType	GarageYrBlt	GarageFinish	GarageCars	GarageArea	GarageQual	GarageCond	PavedDrive	WoodDeckSF	OpenPorchSF	EnclosedPorch	3SsnPorch	ScreenPorch	PoolArea	PoolQC	Fence	MiscFeature	MiscVal	MoSold	YrSold	SaleType	SaleCondition	SalePrice
0	1	60	RL	65.0	8450	Pave	NaN	Reg	Lvl	AllPub	Inside	Gtl	CollgCr	Norm	Norm	1Fam	2Story	7	5	2003	2003	Gable	CompShg	VinylSd	VinylSd	BrkFace	196.0	Gd	TA	PConc	Gd	TA	No	GLQ	706	Unf	0	150	856	GasA	Ex	Y	SBrkr	856	854	0	1710	1	0	2	1	3	1	Gd	8	Typ	0	NaN	Attchd	2003.0	RFn	2	548	TA	TA	Y	0	61	0	0	0	0	NaN	NaN	NaN	0	2	2008	WD	Normal	208500
1	2	20	RL	80.0	9600	Pave	NaN	Reg	Lvl	AllPub	FR2	Gtl	Veenker	Feedr	Norm	1Fam	1Story	6	8	1976	1976	Gable	CompShg	MetalSd	MetalSd	NaN	0.0	TA	TA	CBlock	Gd	TA	Gd	ALQ	978	Unf	0	284	1262	GasA	Ex	Y	SBrkr	1262	0	0	1262	0	1	2	0	3	1	TA	6	Typ	1	TA	Attchd	1976.0	RFn	2	460	TA	TA	Y	298	0	0	0	0	0	NaN	NaN	NaN	0	5	2007	WD	Normal	181500
2	3	60	RL	68.0	11250	Pave	NaN	IR1	Lvl	AllPub	Inside	Gtl	CollgCr	Norm	Norm	1Fam	2Story	7	5	2001	2002	Gable	CompShg	VinylSd	VinylSd	BrkFace	162.0	Gd	TA	PConc	Gd	TA	Mn	GLQ	486	Unf	0	434	920	GasA	Ex	Y	SBrkr	920	866	0	1786	1	0	2	1	3	1	Gd	6	Typ	1	TA	Attchd	2001.0	RFn	2	608	TA	TA	Y	0	42	0	0	0	0	NaN	NaN	NaN	0	9	2008	WD	Normal	223500
3	4	70	RL	60.0	9550	Pave	NaN	IR1	Lvl	AllPub	Corner	Gtl	Crawfor	Norm	Norm	1Fam	2Story	7	5	1915	1970	Gable	CompShg	Wd Sdng	Wd Shng	NaN	0.0	TA	TA	BrkTil	TA	Gd	No	ALQ	216	Unf	0	540	756	GasA	Gd	Y	SBrkr	961	756	0	1717	1	0	1	0	3	1	Gd	7	Typ	1	Gd	Detchd	1998.0	Unf	3	642	TA	TA	Y	0	35	272	0	0	0	NaN	NaN	NaN	0	2	2006	WD	Abnorml	140000
4	5	60	RL	84.0	14260	Pave	NaN	IR1	Lvl	AllPub	FR2	Gtl	NoRidge	Norm	Norm	1Fam	2Story	8	5	2000	2000	Gable	CompShg	VinylSd	VinylSd	BrkFace	350.0	Gd	TA	PConc	Gd	TA	Av	GLQ	655	Unf	0	490	1145	GasA	Ex	Y	SBrkr	1145	1053	0	2198	1	0	2	1	4	1	Gd	9	Typ	1	TA	Attchd	2000.0	RFn	3	836	TA	TA	Y	192	84	0	0	0	0	NaN	NaN	NaN	0	12	2008	WD	Normal	250000

In Data Analysis we will analyze to find out Missing values All the numerical variables Distribution of the numerical variables Categorical variables cardinality of categorical variables Outliers Relationship between independent and dependent features

Missing Values

#check the percentage of nan values present in each feature
#step 1 -the list of features which has missing values
features_with_na=[feature for feature in ds.columns if ds[feature].isnull().sum()>1]
##step-2 print the features name and the percentage of misssing values

for feature in features_with_na:
    print(feature, np.round(ds[feature].isnull().mean(), 4), '%missing values')
    

LotFrontage 0.1774 %missing values
Alley 0.9377 %missing values
MasVnrType 0.5973 %missing values
MasVnrArea 0.0055 %missing values
BsmtQual 0.0253 %missing values
BsmtCond 0.0253 %missing values
BsmtExposure 0.026 %missing values
BsmtFinType1 0.0253 %missing values
BsmtFinType2 0.026 %missing values
FireplaceQu 0.4726 %missing values
GarageType 0.0555 %missing values
GarageYrBlt 0.0555 %missing values
GarageFinish 0.0555 %missing values
GarageQual 0.0555 %missing values
GarageCond 0.0555 %missing values
PoolQC 0.9952 %missing values
Fence 0.8075 %missing values
MiscFeature 0.963 %missing values

Since they are many missing values ,we need to find the relationship between missing values and sales price

for feature in features_with_na:
    data=ds.copy()
    

    #lets make a variable that indicates 1 if the observation was missing or zero
    data[feature]=np.where(data[feature].isnull(),1,0)

    #lets calculate the mean Saleprice where the information missing or present
    data.groupby(feature)['SalePrice'].median().plot.bar(color=['red','skyblue']  # first bar = red,second bar=skyblue
                                                        )
    plt.title(feature)
    plt.show()
    

Here with relation between the missing values and the dependent variables is clearly visible.So we need to replace these nan values with something meaningfull which we will do in the Feature Engineering selection From the above dataset soome of the features like id i not required

print("Id of Houses {}".format(len(ds.Id)))

Id of Houses 1460

Numerical Variables

#list of numerical variables

numerical_features=[feature for feature in ds.columns if ds[feature].dtypes != '0']
print('Number of numerical variables: ', len(numerical_features))

#visualise the numerical variables
ds[numerical_features].head()
                    

Number of numerical variables:  81

	Id	MSSubClass	MSZoning	LotFrontage	LotArea	Street	Alley	LotShape	LandContour	Utilities	LotConfig	LandSlope	Neighborhood	Condition1	Condition2	BldgType	HouseStyle	OverallQual	OverallCond	YearBuilt	YearRemodAdd	RoofStyle	RoofMatl	Exterior1st	Exterior2nd	MasVnrType	MasVnrArea	ExterQual	ExterCond	Foundation	BsmtQual	BsmtCond	BsmtExposure	BsmtFinType1	BsmtFinSF1	BsmtFinType2	BsmtFinSF2	BsmtUnfSF	TotalBsmtSF	Heating	HeatingQC	CentralAir	Electrical	1stFlrSF	2ndFlrSF	LowQualFinSF	GrLivArea	BsmtFullBath	BsmtHalfBath	FullBath	HalfBath	BedroomAbvGr	KitchenAbvGr	KitchenQual	TotRmsAbvGrd	Functional	Fireplaces	FireplaceQu	GarageType	GarageYrBlt	GarageFinish	GarageCars	GarageArea	GarageQual	GarageCond	PavedDrive	WoodDeckSF	OpenPorchSF	EnclosedPorch	3SsnPorch	ScreenPorch	PoolArea	PoolQC	Fence	MiscFeature	MiscVal	MoSold	YrSold	SaleType	SaleCondition	SalePrice
0	1	60	RL	65.0	8450	Pave	NaN	Reg	Lvl	AllPub	Inside	Gtl	CollgCr	Norm	Norm	1Fam	2Story	7	5	2003	2003	Gable	CompShg	VinylSd	VinylSd	BrkFace	196.0	Gd	TA	PConc	Gd	TA	No	GLQ	706	Unf	0	150	856	GasA	Ex	Y	SBrkr	856	854	0	1710	1	0	2	1	3	1	Gd	8	Typ	0	NaN	Attchd	2003.0	RFn	2	548	TA	TA	Y	0	61	0	0	0	0	NaN	NaN	NaN	0	2	2008	WD	Normal	208500
1	2	20	RL	80.0	9600	Pave	NaN	Reg	Lvl	AllPub	FR2	Gtl	Veenker	Feedr	Norm	1Fam	1Story	6	8	1976	1976	Gable	CompShg	MetalSd	MetalSd	NaN	0.0	TA	TA	CBlock	Gd	TA	Gd	ALQ	978	Unf	0	284	1262	GasA	Ex	Y	SBrkr	1262	0	0	1262	0	1	2	0	3	1	TA	6	Typ	1	TA	Attchd	1976.0	RFn	2	460	TA	TA	Y	298	0	0	0	0	0	NaN	NaN	NaN	0	5	2007	WD	Normal	181500
2	3	60	RL	68.0	11250	Pave	NaN	IR1	Lvl	AllPub	Inside	Gtl	CollgCr	Norm	Norm	1Fam	2Story	7	5	2001	2002	Gable	CompShg	VinylSd	VinylSd	BrkFace	162.0	Gd	TA	PConc	Gd	TA	Mn	GLQ	486	Unf	0	434	920	GasA	Ex	Y	SBrkr	920	866	0	1786	1	0	2	1	3	1	Gd	6	Typ	1	TA	Attchd	2001.0	RFn	2	608	TA	TA	Y	0	42	0	0	0	0	NaN	NaN	NaN	0	9	2008	WD	Normal	223500
3	4	70	RL	60.0	9550	Pave	NaN	IR1	Lvl	AllPub	Corner	Gtl	Crawfor	Norm	Norm	1Fam	2Story	7	5	1915	1970	Gable	CompShg	Wd Sdng	Wd Shng	NaN	0.0	TA	TA	BrkTil	TA	Gd	No	ALQ	216	Unf	0	540	756	GasA	Gd	Y	SBrkr	961	756	0	1717	1	0	1	0	3	1	Gd	7	Typ	1	Gd	Detchd	1998.0	Unf	3	642	TA	TA	Y	0	35	272	0	0	0	NaN	NaN	NaN	0	2	2006	WD	Abnorml	140000
4	5	60	RL	84.0	14260	Pave	NaN	IR1	Lvl	AllPub	FR2	Gtl	NoRidge	Norm	Norm	1Fam	2Story	8	5	2000	2000	Gable	CompShg	VinylSd	VinylSd	BrkFace	350.0	Gd	TA	PConc	Gd	TA	Av	GLQ	655	Unf	0	490	1145	GasA	Ex	Y	SBrkr	1145	1053	0	2198	1	0	2	1	4	1	Gd	9	Typ	1	TA	Attchd	2000.0	RFn	3	836	TA	TA	Y	192	84	0	0	0	0	NaN	NaN	NaN	0	12	2008	WD	Normal	250000

Temporal variables (Eg:Datatime Variables)

From the Dataset we have 4 year variables.We have exact information from the datatime variables like no of yeard or no of dates.One example in this specific scenario can be difference in years between the year the house was built and the year house was sold. We will be performing this analysis in the Feature Engineering which is the next video

# List of variables that contain year information
year_feature=[feature for feature in numerical_features if 'Yr' in feature or 'Year' in feature]
year_feature

['YearBuilt', 'YearRemodAdd', 'GarageYrBlt', 'YrSold']

# let's explore the content of these year variables
for feature in year_feature:
    print(feature,ds[feature].unique())

YearBuilt [2003 1976 2001 1915 2000 1993 2004 1973 1931 1939 1965 2005 1962 2006
 1960 1929 1970 1967 1958 1930 2002 1968 2007 1951 1957 1927 1920 1966
 1959 1994 1954 1953 1955 1983 1975 1997 1934 1963 1981 1964 1999 1972
 1921 1945 1982 1998 1956 1948 1910 1995 1991 2009 1950 1961 1977 1985
 1979 1885 1919 1990 1969 1935 1988 1971 1952 1936 1923 1924 1984 1926
 1940 1941 1987 1986 2008 1908 1892 1916 1932 1918 1912 1947 1925 1900
 1980 1989 1992 1949 1880 1928 1978 1922 1996 2010 1946 1913 1937 1942
 1938 1974 1893 1914 1906 1890 1898 1904 1882 1875 1911 1917 1872 1905]
YearRemodAdd [2003 1976 2002 1970 2000 1995 2005 1973 1950 1965 2006 1962 2007 1960
 2001 1967 2004 2008 1997 1959 1990 1955 1983 1980 1966 1963 1987 1964
 1972 1996 1998 1989 1953 1956 1968 1981 1992 2009 1982 1961 1993 1999
 1985 1979 1977 1969 1958 1991 1971 1952 1975 2010 1984 1986 1994 1988
 1954 1957 1951 1978 1974]
GarageYrBlt [2003. 1976. 2001. 1998. 2000. 1993. 2004. 1973. 1931. 1939. 1965. 2005.
 1962. 2006. 1960. 1991. 1970. 1967. 1958. 1930. 2002. 1968. 2007. 2008.
 1957. 1920. 1966. 1959. 1995. 1954. 1953.   nan 1983. 1977. 1997. 1985.
 1963. 1981. 1964. 1999. 1935. 1990. 1945. 1987. 1989. 1915. 1956. 1948.
 1974. 2009. 1950. 1961. 1921. 1900. 1979. 1951. 1969. 1936. 1975. 1971.
 1923. 1984. 1926. 1955. 1986. 1988. 1916. 1932. 1972. 1918. 1980. 1924.
 1996. 1940. 1949. 1994. 1910. 1978. 1982. 1992. 1925. 1941. 2010. 1927.
 1947. 1937. 1942. 1938. 1952. 1928. 1922. 1934. 1906. 1914. 1946. 1908.
 1929. 1933.]
YrSold [2008 2007 2006 2009 2010]

 ## lets analyze the temporal datetime variables
## we will check whether there is a relation between year the house is sold and

ds.groupby('YrSold')['SalePrice'].median().plot()
plt.xlabel('Year Sold')
plt.ylabel('Median House Price')
plt.title('House Price vs YearSold')

Text(0.5, 1.0, 'House Price vs YearSold')

year_feature

['YearBuilt', 'YearRemodAdd', 'GarageYrBlt', 'YrSold']

## Here we will compare the differnece between all years feature with SalePrice

for feature in year_feature:
    if feature!='YrSold':
        data=ds.copy()
        ##we will capture the difference between your variables and year of the house sold for
        data[feature]=data['YrSold']-data[feature]

        plt.scatter(data[feature],data['SalePrice'])
        plt.xlabel(feature)
        plt.ylabel('SalePrice')
        plt.show()
    

## Numerical Variables are usually of 2 types
## 1.Continous variables and Discrete variables

discrete_feature=[feature for feature in numerical_features if len(ds[feature].unique())<25 and feature not in year_feature] 
print("Discrete Variables Count: {}".format(len(discrete_feature)))

Discrete Variables Count: 59

discrete_feature

['MSSubClass',
 'MSZoning',
 'Street',
 'Alley',
 'LotShape',
 'LandContour',
 'Utilities',
 'LotConfig',
 'LandSlope',
 'Condition1',
 'Condition2',
 'BldgType',
 'HouseStyle',
 'OverallQual',
 'OverallCond',
 'RoofStyle',
 'RoofMatl',
 'Exterior1st',
 'Exterior2nd',
 'MasVnrType',
 'ExterQual',
 'ExterCond',
 'Foundation',
 'BsmtQual',
 'BsmtCond',
 'BsmtExposure',
 'BsmtFinType1',
 'BsmtFinType2',
 'Heating',
 'HeatingQC',
 'CentralAir',
 'Electrical',
 'LowQualFinSF',
 'BsmtFullBath',
 'BsmtHalfBath',
 'FullBath',
 'HalfBath',
 'BedroomAbvGr',
 'KitchenAbvGr',
 'KitchenQual',
 'TotRmsAbvGrd',
 'Functional',
 'Fireplaces',
 'FireplaceQu',
 'GarageType',
 'GarageFinish',
 'GarageCars',
 'GarageQual',
 'GarageCond',
 'PavedDrive',
 '3SsnPorch',
 'PoolArea',
 'PoolQC',
 'Fence',
 'MiscFeature',
 'MiscVal',
 'MoSold',
 'SaleType',
 'SaleCondition']

ds[discrete_feature].head()

	MSSubClass	MSZoning	Street	Alley	LotShape	LandContour	Utilities	LotConfig	LandSlope	Condition1	Condition2	BldgType	HouseStyle	OverallQual	OverallCond	RoofStyle	RoofMatl	Exterior1st	Exterior2nd	MasVnrType	ExterQual	ExterCond	Foundation	BsmtQual	BsmtCond	BsmtExposure	BsmtFinType1	BsmtFinType2	Heating	HeatingQC	CentralAir	Electrical	LowQualFinSF	BsmtFullBath	BsmtHalfBath	FullBath	HalfBath	BedroomAbvGr	KitchenAbvGr	KitchenQual	TotRmsAbvGrd	Functional	Fireplaces	FireplaceQu	GarageType	GarageFinish	GarageCars	GarageQual	GarageCond	PavedDrive	3SsnPorch	PoolArea	PoolQC	Fence	MiscFeature	MiscVal	MoSold	SaleType	SaleCondition
0	60	RL	Pave	NaN	Reg	Lvl	AllPub	Inside	Gtl	Norm	Norm	1Fam	2Story	7	5	Gable	CompShg	VinylSd	VinylSd	BrkFace	Gd	TA	PConc	Gd	TA	No	GLQ	Unf	GasA	Ex	Y	SBrkr	0	1	0	2	1	3	1	Gd	8	Typ	0	NaN	Attchd	RFn	2	TA	TA	Y	0	0	NaN	NaN	NaN	0	2	WD	Normal
1	20	RL	Pave	NaN	Reg	Lvl	AllPub	FR2	Gtl	Feedr	Norm	1Fam	1Story	6	8	Gable	CompShg	MetalSd	MetalSd	NaN	TA	TA	CBlock	Gd	TA	Gd	ALQ	Unf	GasA	Ex	Y	SBrkr	0	0	1	2	0	3	1	TA	6	Typ	1	TA	Attchd	RFn	2	TA	TA	Y	0	0	NaN	NaN	NaN	0	5	WD	Normal
2	60	RL	Pave	NaN	IR1	Lvl	AllPub	Inside	Gtl	Norm	Norm	1Fam	2Story	7	5	Gable	CompShg	VinylSd	VinylSd	BrkFace	Gd	TA	PConc	Gd	TA	Mn	GLQ	Unf	GasA	Ex	Y	SBrkr	0	1	0	2	1	3	1	Gd	6	Typ	1	TA	Attchd	RFn	2	TA	TA	Y	0	0	NaN	NaN	NaN	0	9	WD	Normal
3	70	RL	Pave	NaN	IR1	Lvl	AllPub	Corner	Gtl	Norm	Norm	1Fam	2Story	7	5	Gable	CompShg	Wd Sdng	Wd Shng	NaN	TA	TA	BrkTil	TA	Gd	No	ALQ	Unf	GasA	Gd	Y	SBrkr	0	1	0	1	0	3	1	Gd	7	Typ	1	Gd	Detchd	Unf	3	TA	TA	Y	0	0	NaN	NaN	NaN	0	2	WD	Abnorml
4	60	RL	Pave	NaN	IR1	Lvl	AllPub	FR2	Gtl	Norm	Norm	1Fam	2Story	8	5	Gable	CompShg	VinylSd	VinylSd	BrkFace	Gd	TA	PConc	Gd	TA	Av	GLQ	Unf	GasA	Ex	Y	SBrkr	0	1	0	2	1	4	1	Gd	9	Typ	1	TA	Attchd	RFn	3	TA	TA	Y	0	0	NaN	NaN	NaN	0	12	WD	Normal

#Let's find the relationship between them and Sale Price

for feature in discrete_feature:
    data=ds.copy()
    data.groupby(feature)['SalePrice'].median().plot.bar(color=['red','blue','green','orange'])
    plt.xlabel(feature)
    plt.ylabel('SalePrice')
    plt.title(feature)
    plt.show()

## There is a relationship between variable number and Saleprice

Continous Variable

continous_feature=[feature for feature in numerical_features if feature not in discrete_feature+year_feature+['Id']]
print("Continous feature count {}".format(len(continous_feature)))

Continous feature count 17

#Lets analyze the continous values by creating histograms to understand

for feature in continous_feature:
    data=ds.copy()
    data[feature].hist(bins=25)
    plt.xlabel(feature)
    plt.ylabel("Count")
    plt.title(feature)
    plt.show()

Exploratory Data Analiysis

## we will be using logarithmic transformation

for feature in continous_feature:
    data=ds.copy()

    # skip if column is not numeric
    if data[feature].dtype == 'object':
        continue

    # skip if column has 0 values
    if 0 in data[feature].values:
        continue

    
    else: 
        data[feature]=np.log(data[feature])
        data['SalePrice']=np.log(data['SalePrice'])
        plt.scatter(data[feature],data['SalePrice'])
        plt.xlabel(feature)
        plt.ylabel('SalePrice')
        plt.title(feature)
        plt.show()

Outliers

for feature in continous_feature:
    data=ds.copy()

    # skip if column is not numeric
    if data[feature].dtype == 'object':
        continue

    if 0 in data[feature].values:
        continue

    
    else: 
        data[feature]=np.log(data[feature])
        data.boxplot(column=feature)
        plt.ylabel(feature)
        plt.title(feature)
        plt.show()

Categorical Variables

categorical_features=[feature for feature in ds.columns if data[feature].dtype=='O']
categorical_features                      

['MSZoning',
 'Street',
 'Alley',
 'LotShape',
 'LandContour',
 'Utilities',
 'LotConfig',
 'LandSlope',
 'Neighborhood',
 'Condition1',
 'Condition2',
 'BldgType',
 'HouseStyle',
 'RoofStyle',
 'RoofMatl',
 'Exterior1st',
 'Exterior2nd',
 'MasVnrType',
 'ExterQual',
 'ExterCond',
 'Foundation',
 'BsmtQual',
 'BsmtCond',
 'BsmtExposure',
 'BsmtFinType1',
 'BsmtFinType2',
 'Heating',
 'HeatingQC',
 'CentralAir',
 'Electrical',
 'KitchenQual',
 'Functional',
 'FireplaceQu',
 'GarageType',
 'GarageFinish',
 'GarageQual',
 'GarageCond',
 'PavedDrive',
 'PoolQC',
 'Fence',
 'MiscFeature',
 'SaleType',
 'SaleCondition']

ds[categorical_features].head()

	MSZoning	Street	Alley	LotShape	LandContour	Utilities	LotConfig	LandSlope	Neighborhood	Condition1	Condition2	BldgType	HouseStyle	RoofStyle	RoofMatl	Exterior1st	Exterior2nd	MasVnrType	ExterQual	ExterCond	Foundation	BsmtQual	BsmtCond	BsmtExposure	BsmtFinType1	BsmtFinType2	Heating	HeatingQC	CentralAir	Electrical	KitchenQual	Functional	FireplaceQu	GarageType	GarageFinish	GarageQual	GarageCond	PavedDrive	PoolQC	Fence	MiscFeature	SaleType	SaleCondition
0	RL	Pave	NaN	Reg	Lvl	AllPub	Inside	Gtl	CollgCr	Norm	Norm	1Fam	2Story	Gable	CompShg	VinylSd	VinylSd	BrkFace	Gd	TA	PConc	Gd	TA	No	GLQ	Unf	GasA	Ex	Y	SBrkr	Gd	Typ	NaN	Attchd	RFn	TA	TA	Y	NaN	NaN	NaN	WD	Normal
1	RL	Pave	NaN	Reg	Lvl	AllPub	FR2	Gtl	Veenker	Feedr	Norm	1Fam	1Story	Gable	CompShg	MetalSd	MetalSd	NaN	TA	TA	CBlock	Gd	TA	Gd	ALQ	Unf	GasA	Ex	Y	SBrkr	TA	Typ	TA	Attchd	RFn	TA	TA	Y	NaN	NaN	NaN	WD	Normal
2	RL	Pave	NaN	IR1	Lvl	AllPub	Inside	Gtl	CollgCr	Norm	Norm	1Fam	2Story	Gable	CompShg	VinylSd	VinylSd	BrkFace	Gd	TA	PConc	Gd	TA	Mn	GLQ	Unf	GasA	Ex	Y	SBrkr	Gd	Typ	TA	Attchd	RFn	TA	TA	Y	NaN	NaN	NaN	WD	Normal
3	RL	Pave	NaN	IR1	Lvl	AllPub	Corner	Gtl	Crawfor	Norm	Norm	1Fam	2Story	Gable	CompShg	Wd Sdng	Wd Shng	NaN	TA	TA	BrkTil	TA	Gd	No	ALQ	Unf	GasA	Gd	Y	SBrkr	Gd	Typ	Gd	Detchd	Unf	TA	TA	Y	NaN	NaN	NaN	WD	Abnorml
4	RL	Pave	NaN	IR1	Lvl	AllPub	FR2	Gtl	NoRidge	Norm	Norm	1Fam	2Story	Gable	CompShg	VinylSd	VinylSd	BrkFace	Gd	TA	PConc	Gd	TA	Av	GLQ	Unf	GasA	Ex	Y	SBrkr	Gd	Typ	TA	Attchd	RFn	TA	TA	Y	NaN	NaN	NaN	WD	Normal

for feature in categorical_features:
    print('The feature is {} and number of categories are {}'.format(feature,len(ds[feature].unique())))

The feature is MSZoning and number of categories are 5
The feature is Street and number of categories are 2
The feature is Alley and number of categories are 3
The feature is LotShape and number of categories are 4
The feature is LandContour and number of categories are 4
The feature is Utilities and number of categories are 2
The feature is LotConfig and number of categories are 5
The feature is LandSlope and number of categories are 3
The feature is Neighborhood and number of categories are 25
The feature is Condition1 and number of categories are 9
The feature is Condition2 and number of categories are 8
The feature is BldgType and number of categories are 5
The feature is HouseStyle and number of categories are 8
The feature is RoofStyle and number of categories are 6
The feature is RoofMatl and number of categories are 8
The feature is Exterior1st and number of categories are 15
The feature is Exterior2nd and number of categories are 16
The feature is MasVnrType and number of categories are 4
The feature is ExterQual and number of categories are 4
The feature is ExterCond and number of categories are 5
The feature is Foundation and number of categories are 6
The feature is BsmtQual and number of categories are 5
The feature is BsmtCond and number of categories are 5
The feature is BsmtExposure and number of categories are 5
The feature is BsmtFinType1 and number of categories are 7
The feature is BsmtFinType2 and number of categories are 7
The feature is Heating and number of categories are 6
The feature is HeatingQC and number of categories are 5
The feature is CentralAir and number of categories are 2
The feature is Electrical and number of categories are 6
The feature is KitchenQual and number of categories are 4
The feature is Functional and number of categories are 7
The feature is FireplaceQu and number of categories are 6
The feature is GarageType and number of categories are 7
The feature is GarageFinish and number of categories are 4
The feature is GarageQual and number of categories are 6
The feature is GarageCond and number of categories are 6
The feature is PavedDrive and number of categories are 3
The feature is PoolQC and number of categories are 4
The feature is Fence and number of categories are 5
The feature is MiscFeature and number of categories are 5
The feature is SaleType and number of categories are 9
The feature is SaleCondition and number of categories are 6

 ## Findout the relationship between categorical variables and dependent feature i.e Salesprice

for feature in categorical_features:
    data=ds.copy()
    data.groupby(feature)['SalePrice'].median().plot.bar()
    plt.xlabel(feature)
    plt.ylabel('SalePrice')
    plt.title(feature)
    plt.show()